Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4f7ab1f6",
+   "id": "8bce29cb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6fa99933",
+   "id": "8e0996d5",
    "metadata": {},
    "source": [
     "\n",

_downloads/36608d2d57f623ba3a623e0c947a8c3e/data_tutorial.ipynb

@@ -183,7 +183,7 @@
    "source": [
     "import os\n",
     "import pandas as pd\n",
-    "from torchvision.io import read_image\n",
+    "from torchvision.io import decode_image\n",
     "\n",
     "class CustomImageDataset(Dataset):\n",
     "    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):\n",
@@ -197,7 +197,7 @@
     "\n",
     "    def __getitem__(self, idx):\n",
     "        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])\n",
-    "        image = read_image(img_path)\n",
+    "        image = decode_image(img_path)\n",
     "        label = self.img_labels.iloc[idx, 1]\n",
     "        if self.transform:\n",
     "            image = self.transform(image)\n",
@@ -276,7 +276,7 @@
     "The \\_\\_[getitem](#getitem__) function loads and returns a sample from\n",
     "the dataset at the given index `idx`. Based on the index, it identifies\n",
     "the image\\'s location on disk, converts that to a tensor using\n",
-    "`read_image`, retrieves the corresponding label from the csv data in\n",
+    "`decode_image`, retrieves the corresponding label from the csv data in\n",
     "`self.img_labels`, calls the transform functions on them (if\n",
     "applicable), and returns the tensor image and corresponding label in a\n",
     "tuple.\n"

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cb506594",
+   "id": "b5bc50c7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "593311b9",
+   "id": "5b7307ba",
    "metadata": {},
    "source": [
     "\n",

_downloads/56e3f440fc204e02856f8889c226d2d1/data_tutorial.py

@@ -120,7 +120,7 @@
 
 import os
 import pandas as pd
-from torchvision.io import read_image
+from torchvision.io import decode_image
 
 class CustomImageDataset(Dataset):
     def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
@@ -134,7 +134,7 @@ def __len__(self):
 
     def __getitem__(self, idx):
         img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
-        image = read_image(img_path)
+        image = decode_image(img_path)
         label = self.img_labels.iloc[idx, 1]
         if self.transform:
             image = self.transform(image)
@@ -184,7 +184,7 @@ def __len__(self):
 # ^^^^^^^^^^^^^^^^^^^^
 #
 # The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``.
-# Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the
+# Based on the index, it identifies the image's location on disk, converts that to a tensor using ``decode_image``, retrieves the
 # corresponding label from the csv data in ``self.img_labels``, calls the transform functions on them (if applicable), and returns the
 # tensor image and corresponding label in a tuple.
 

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a324b160",
+   "id": "55aafed1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61128946",
+   "id": "0b865ee4",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5fe79bda",
+   "id": "61d8ceeb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d32f8e08",
+   "id": "0b7b34be",
    "metadata": {},
    "source": [
     "\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "df9ab744",
+   "id": "eb621ebe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "145b47d7",
+   "id": "a9898a03",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47d8d15a",
+   "id": "51f716f0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09dfb20c",
+   "id": "8e112120",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b6e94f78",
+   "id": "2d3ad1fc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27b1a2e9",
+   "id": "5062fb08",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f29fa0ff",
+   "id": "b577a5bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "171d6dbb",
+   "id": "aeb5874a",
    "metadata": {},
    "source": [
     "\n",